Signature:
px.bar(
data_frame=None,
x=None,
y=None,
color=None,
pattern_shape=None,
facet_row=None,
facet_col=None,
facet_col_wrap=0,
facet_row_spacing=None,
facet_col_spacing=None,
hover_name=None,
hover_data=None,
custom_data=None,
text=None,
base=None,
error_x=None,
error_x_minus=None,
error_y=None,
error_y_minus=None,
animation_frame=None,
animation_group=None,
category_orders=None,
labels=None,
color_discrete_sequence=None,
color_discrete_map=None,
color_continuous_scale=None,
pattern_shape_sequence=None,
pattern_shape_map=None,
range_color=None,
color_continuous_midpoint=None,
opacity=None,
orientation=None,
barmode='relative',
log_x=False,
log_y=False,
range_x=None,
range_y=None,
text_auto=False,
title=None,
template=None,
width=None,
height=None,
) -> plotly.graph_objs._figure.Figure
Docstring:
In a bar plot, each row of `data_frame` is represented as a rectangular
mark.
Parameters
----------
data_frame: DataFrame or array-like or dict
This argument needs to be passed for column names (and not keyword
names) to be used. Array-like and dict are tranformed internally to a
pandas DataFrame. Optional: if missing, a DataFrame gets constructed
under the hood using the other arguments.
x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the x axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position marks along the y axis in cartesian coordinates. Either `x` or
`y` can optionally be a list of column references or array_likes, in
which case the data will be treated as if it were 'wide' rather than
'long'.
color: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign color to marks.
pattern_shape: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign pattern shapes to marks.
facet_row: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the vertical direction.
facet_col: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to facetted subplots in the horizontal direction.
facet_col_wrap: int
Maximum number of facet columns. Wraps the column variable at this
width, so that the column facets span multiple rows. Ignored if 0, and
forced to 0 if `facet_row` or a `marginal` is set.
facet_row_spacing: float between 0 and 1
Spacing between facet rows, in paper units. Default is 0.03 or 0.0.7
when facet_col_wrap is used.
facet_col_spacing: float between 0 and 1
Spacing between facet columns, in paper units Default is 0.02.
hover_name: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in bold
in the hover tooltip.
hover_data: list of str or int, or Series or array-like, or dict
Either a list of names of columns in `data_frame`, or pandas Series, or
array_like objects or a dict with column names as keys, with values
True (for default formatting) False (in order to remove this column
from hover information), or a formatting string, for example ':.3f' or
'|%a' or list-like data to appear in the hover tooltip or tuples with a
bool or formatting string as first element, and list-like data to
appear in hover as second element Values from these columns appear as
extra data in the hover tooltip.
custom_data: list of str or int, or Series or array-like
Either names of columns in `data_frame`, or pandas Series, or
array_like objects Values from these columns are extra data, to be used
in widgets or Dash callbacks for example. This data is not user-visible
but is included in events emitted by the figure (lasso selection etc.)
text: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like appear in the
figure as text labels.
base: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
position the base of the bar.
error_x: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars. If `error_x_minus` is `None`, error bars will
be symmetrical, otherwise `error_x` is used for the positive direction
only.
error_x_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size x-axis error bars in the negative direction. Ignored if `error_x`
is `None`.
error_y: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars. If `error_y_minus` is `None`, error bars will
be symmetrical, otherwise `error_y` is used for the positive direction
only.
error_y_minus: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
size y-axis error bars in the negative direction. Ignored if `error_y`
is `None`.
animation_frame: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
assign marks to animation frames.
animation_group: str or int or Series or array-like
Either a name of a column in `data_frame`, or a pandas Series or
array_like object. Values from this column or array_like are used to
provide object-constancy across animation frames: rows with matching
`animation_group`s will be treated as if they describe the same object
in each frame.
category_orders: dict with str keys and list of str values (default `{}`)
By default, in Python 3.6+, the order of categorical values in axes,
legends and facets depends on the order in which these values are first
encountered in `data_frame` (and no order is guaranteed by default in
Python below 3.6). This parameter is used to force a specific ordering
of values per column. The keys of this dict should correspond to column
names, and the values should be lists of strings corresponding to the
specific display order desired.
labels: dict with str keys and str values (default `{}`)
By default, column names are used in the figure for axis titles, legend
entries and hovers. This parameter allows this to be overridden. The
keys of this dict should correspond to column names, and the values
should correspond to the desired label to be displayed.
color_discrete_sequence: list of str
Strings should define valid CSS-colors. When `color` is set and the
values in the corresponding column are not numeric, values in that
column are assigned colors by cycling through `color_discrete_sequence`
in the order described in `category_orders`, unless the value of
`color` is a key in `color_discrete_map`. Various useful color
sequences are available in the `plotly.express.colors` submodules,
specifically `plotly.express.colors.qualitative`.
color_discrete_map: dict with str keys and str values (default `{}`)
String values should define valid CSS-colors Used to override
`color_discrete_sequence` to assign a specific colors to marks
corresponding with specific values. Keys in `color_discrete_map` should
be values in the column denoted by `color`. Alternatively, if the
values of `color` are valid colors, the string `'identity'` may be
passed to cause them to be used directly.
color_continuous_scale: list of str
Strings should define valid CSS-colors This list is used to build a
continuous color scale when the column denoted by `color` contains
numeric data. Various useful color scales are available in the
`plotly.express.colors` submodules, specifically
`plotly.express.colors.sequential`, `plotly.express.colors.diverging`
and `plotly.express.colors.cyclical`.
pattern_shape_sequence: list of str
Strings should define valid plotly.js patterns-shapes. When
`pattern_shape` is set, values in that column are assigned patterns-
shapes by cycling through `pattern_shape_sequence` in the order
described in `category_orders`, unless the value of `pattern_shape` is
a key in `pattern_shape_map`.
pattern_shape_map: dict with str keys and str values (default `{}`)
Strings values define plotly.js patterns-shapes. Used to override
`pattern_shape_sequences` to assign a specific patterns-shapes to lines
corresponding with specific values. Keys in `pattern_shape_map` should
be values in the column denoted by `pattern_shape`. Alternatively, if
the values of `pattern_shape` are valid patterns-shapes names, the
string `'identity'` may be passed to cause them to be used directly.
range_color: list of two numbers
If provided, overrides auto-scaling on the continuous color scale.
color_continuous_midpoint: number (default `None`)
If set, computes the bounds of the continuous color scale to have the
desired midpoint. Setting this value is recommended when using
`plotly.express.colors.diverging` color scales as the inputs to
`color_continuous_scale`.
opacity: float
Value between 0 and 1. Sets the opacity for markers.
orientation: str, one of `'h'` for horizontal or `'v'` for vertical.
(default `'v'` if `x` and `y` are provided and both continous or both
categorical, otherwise `'v'`(`'h'`) if `x`(`y`) is categorical and
`y`(`x`) is continuous, otherwise `'v'`(`'h'`) if only `x`(`y`) is
provided)
barmode: str (default `'relative'`)
One of `'group'`, `'overlay'` or `'relative'` In `'relative'` mode,
bars are stacked above zero for positive values and below zero for
negative values. In `'overlay'` mode, bars are drawn on top of one
another. In `'group'` mode, bars are placed beside each other.
log_x: boolean (default `False`)
If `True`, the x-axis is log-scaled in cartesian coordinates.
log_y: boolean (default `False`)
If `True`, the y-axis is log-scaled in cartesian coordinates.
range_x: list of two numbers
If provided, overrides auto-scaling on the x-axis in cartesian
coordinates.
range_y: list of two numbers
If provided, overrides auto-scaling on the y-axis in cartesian
coordinates.
text_auto: bool or string (default `False`)
If `True` or a string, the x or y or z values will be displayed as
text, depending on the orientation A string like `'.2f'` will be
interpreted as a `texttemplate` numeric formatting directive.
title: str
The figure title.
template: str or dict or plotly.graph_objects.layout.Template instance
The figure template name (must be a key in plotly.io.templates) or
definition.
width: int (default `None`)
The figure width in pixels.
height: int (default `None`)
The figure height in pixels.
Returns
-------
plotly.graph_objects.Figure
File: c:\users\jesse\anaconda3\envs\til6022\lib\site-packages\plotly\express\_chart_types.py
Type: function
